An interdisciplinary approach is necessary to discover and match meaning
dynamically in a world of increasingly large data. This workshop aims
to bring together practitioners from academia, industry and government
for interaction and discussion. The workshop will feature:

The problem of semantic alignment - that of two systems failing to
understand one another when their representations are not identical -
occurs in a huge variety of areas: Linked Data, database integration,
e-science, multi-agent systems, information retrieval over structured
data; anywhere, in fact, where semantics or a shared structure are
necessary but centralised control over the schema of the data sources is
undesirable or impractical. Yet this is increasingly a critical problem
in the world of large scale data, particularly as more and more of this
kind of data is available over the Web.

In order to interact successfully in an open and heterogeneous
environment, being able to dynamically and adaptively integrate large
and heterogeneous data from the Web "on the go" is necessary. This may
not be a precise process but a matter of finding a good enough
integration to allow interaction to proceed successfully, even if a
complete solution is impossible.

Considerable success has already been achieved in the field of ontology
matching and merging, but the application of these techniques - often
developed for static environments - to the dynamic integration of
large-scale data has not been well studied.

Presenting the results of such dynamic integration to both end-users and
database administrators - while providing quality assurance and
provenance - is not yet a feature of many deployed systems. To make
matters more difficult, on the Web there are massive amounts of
information available online that could be integrated, but this
information is often chaotically organised, stored in a wide variety of
data-formats, and difficult to interpret.

This area has been of interest in academia for some time, and is
becoming increasingly important in industry and - thanks to open data
efforts and other initiatives - to government as well. The aim of this
workshop is to bring together practitioners from academia, industry and
government who are involved in all aspects of this field: from those
developing, curating and using Linked Data, to those focusing on
matching and merging techniques.

LHD-11 invites submissions of both full length papers of no more than 6
pages and position papers of 1-3 pages. Authors of full-papers which are
considered to be both of a high quality and of broad interest to most
attendees will be invited to give full presentations; authors of more
position papers will be invited to participate in "group panels" and in
a poster session.

All accepted papers (both position and full length papers) will be
published as part of the IJCAI workshop proceedings, and will be
available online from the workshop website. After the workshop, we will
be publishing a special issue of the Artificial Intelligence Review and
authors of the best quality submissions will be invited to submit
extended versions of their papers (subject to the overall standard of
submissions being appropriately high).